Europe
Deus Ex Machina: Fa.i.th in the Age of Artificial Intelligence
The artificial intelligence vocabulary has always been a phantasmagorical entanglement of messianic dreams and apocalyptic visions, repurposing words such as "transcendence", "mission", "evangelist", and "prophet". Elon Musk himself went as far as to say in 2014 that "with artificial intelligence we are summoning the demon", later speaking of an AI which would "rise in status to become more like a god, something that can write its own bible and draw humans to worship it". These hyperboles may be no more than men and women at a loss for words, seeking refuge in a familiar metaphysical lexicon, as Einstein and Hawking once did. After all, America has always benefited from nondenominational religious themes as part of its national identity, which may have seeped through to its everyday language. And though many in the tech world have indeed been quick to dismiss such talks, a religious discussion may yet have its place in the AI discourse, if only for the sake of their similarities.
AI Helps Cities Predict Natural Disasters
Give artificial intelligence some of the credit. Hydro One used an electrical-outage prediction tool developed by International Business Machines Corp. IBM -1.14% that combines AI technology and the resources of IBM's Weather Co. subsidiary. The tool helped predict the severity of the storm and the locations that would be hardest hit, so Hydro One knew where to position 1,400 front-line staff who were needed to restore power and to handle the nearly 130,000 customer calls that came in during the outage. IBM's outage-prediction tool is also being used, with 70% accuracy, by other cities throughout North America to predict power outages as far in advance as 72 hours before storms are expected.
Merkel warns of AI brain drain to foreign tech companies
Private companies are after market monopolies, German chancellor says. BERLIN -- Germany is at risk of losing its best experts on artificial intelligence to the world's private tech giants, Chancellor Angela Merkel warned Wednesday. "The big internet companies are really trying to get a market monopoly here," Merkel said during a panel discussion when asked what she has identified as some of the most pressing digital policy issues since taking office for a fourth term about three months ago. At the same time, the country's "startups still don't have enough ways to get funding when they get bigger and there's always a danger that they're snapped up from abroad," Merkel added. Earlier this week, Germany's leading artificial intelligence (AI) startups warned in a report that the country is falling behind in the research of artificial intelligence while at the same time, a lack of public and private investment is holding back the development of cutting-edge AI applications.
Babylon claims its chatbot beats GPs at medical exam
Claims that a chatbot can diagnose medical conditions as accurately as a GP have sparked a row between the software's creators and UK doctors. Babylon, the company behind the NHS GP at Hand app, says its follow-up software achieves medical exam scores that are on-par with human doctors. It revealed the artificial intelligence bot at an event held at the Royal College of Physicians. But another medical professional body said it doubted the AI's abilities. "No app or algorithm will be able to do what a GP does," said the Royal College of General Practitioners.
Are autonomous data centers on the horizon?
At some point in the not-too-distant future, artificial intelligence (AI) will drive our cars, write our programming code, and optimize how we do business. Data centers, too, will be unable to escape this trend. Thanks to machine learning technology, companies and data center operators will be able to coordinate and manage increasingly complex machines, infrastructures, and data more effectively than ever before, even as their numbers and data volumes continue to rise. Are completely autonomous, self-repairing data centers on the horizon? The data center is the backbone of the digital revolution.
'You cannot be serious': IBM taps emotions for...
An AI will use the emotional reactions of top tennis players at next month's Wimbledon tournament to create highlight reels for each match. IBM's robot'Watson' has been upgraded to watch the gesticulations of players - such as fist-pumps of elation or cries of frustration. It combines these reactions with an analysis of crowd noise, players' movements and match data to pick out key highlights for fans to watch. An AI will use the emotional reactions of top tennis players at next month's Wimbledon tournament to create highlight reels for each match. Clips are then sent to the organiser of Wimbledon, the All England Lawn Tennis Club, for uploading on social media as well as its apps and official website.
What our lives could look like on Mars and the moon
A vision of what life on Mars and the moon could look like has been revealed in a series of incredible concept images. The stunning shots reveal the 3D-printed homes and automated vehicles that could one day cover the surface of the planet. They were created by the firm set up by designer Norman Foster, perhaps best known in the technology world for his work on Apple's newly constructed'spaceship' campus in Cupertino, California. A vision of what life on Mars and the moon could look like has been revealed in an incredible series of concept images. The stunning shots reveal the 3D-printed houses and automated vehicles that will scatter the surface of future Mars and lunar colonies.
Machine learning predicts World Cup winner
The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.